1. Field of the Invention
The present invention relates to an image processing method and an image processing apparatus, and more particularly to the method and the apparatus which can reduce noises existing on an image.
2. Description of the Related Art
At present, image processing technology has come to be utilized in various fields.
Image processing is executed in order to cope with, e.g., the degradation and amelioration of an image acquired by a video tape recorder, a digital camera or the like, or it is executed for the purpose of, e.g., clearly grasping the pattern or structure itself of a structural object in order to inspect if the structure is manufactured as designed.
Also in various medical image diagnostic equipment such as an X-ray CT equipment, SPECT equipment and MRI equipment, multifarious image processing aspects are executed. Regarding the execution of, e.g., the depiction of a blood stream or contrast medium stream, the sampling of a morbid part, or the contouring of internal organs, benefits are extensively accepted.
The image processing technology consists of various constituent techniques such as a noise suppression technique, feature extraction technique and pattern recognition technique, and the individual techniques are utilized alone or in an appropriate combination. Incidentally, among such constituent techniques, especially a technique which reduces random noises contained in an image is an indispensable one for more clearly reproducing an object subjected to imaging, reconstruction or the like.
Further improvements, however, are required of the prior-art image processing technology, especially the noise reduction technique. By way of example, so-called “smoothing” is widely known as the noise reduction technique. The smoothing is such that, when an input value f(i, j) exists for a certain pixel(i, j), an average density in the vicinity of the pixel(i, j) is afforded as an output value g(i, j) for the pixel(i, j). Concretely, assuming that n×n pixels in the vicinity of the pixel(i, j) are used, the output value g(i, j) is found as:
                              g          ⁡                      (                          i              ,              j                        )                          =                              ∑                          k              =              a                        b                    ⁢                                    ∑                              j                =                c                            d                        ⁢                                          1                                                      (                                          b                      -                      a                      +                      1                                        )                                    ⁢                                      (                                          d                      -                      c                      +                      1                                        )                                                              ·                              f                ⁡                                  (                                                            i                      +                      k                                        ,                                          j                      +                      1                                                        )                                                                                        (        1        )            Here, letters a, b, c and d in the above equation (1) denote integers. Besides, 1/(b−a+1)(d−c+1) in the equation (1) is a so-called “weight”. Incidentally, FIG. 13 shows a case where a, b, c and d=−1, 1, −1 and 1.
Meanwhile, it is generally known that, when the average value of n samples independently taken from the distribution of a population whose variance is σ2 is computed, the variance of the average value becomes σ2/n. According to the equation (1), therefore, the “population” and “its variance σ2” mentioned above correspond to a probability distribution whose random variable is a component ascribable to noises contained in the value of each pixel (i, j), and the variance thereof, respectively, so that the component of the value f(i, j) of each pixel attributed to the noises can be lowered.
Merely by simply applying the smoothing, however, so-called “edge obscurity” appears, the spatial resolution of an image is spoilt, and the impression is given that the whole image is unsharp. Regarding, for example, the medical image referred to above, even in a case where a minute blood vessel structure is to be depicted with the least possible noise, the noise suppression processing based on the equation (1) executes the averaging (smoothing) also in pixels which do not originally depict the blood vessel structure. Therefore, admitting that the noises are suppressed, a contrast expressive of the blood vessel structure is also lowered by the smoothing, so that the depiction of the minute blood vessel structure sometimes becomes difficult.
The present invention has been made in view of the above circumstances, and has for its object to provide an image processing method and an image processing apparatus which can satisfactorily suppress noises without giving rise to the unsharpness of an image, and which can effectively contribute to other image processing techniques, for example, a pattern recognition technique.